In this blog, we outline the essential steps for migrating ETL workloads and EDW from Snowflake to Databricks. From data migration to report modernization, we break down five key phases for a seamless and efficient transition to Databricks.

Funny or not, building a secured, governed and scalable data platform that supports multiple types of use cases along with the data management processes and practices is very similar to building a skyscraper - the higher the building grows and supports more units and people, the complexity increases.

This guide will help you understand the complexities of Databricks, ensuring your data skyscraper stands tall and proud.

In the intricate landscape of migration planning, it is imperative to map processes and prioritize them according to their criticality. This implies a strategic process to determine the sequence in which processes should be migrated according to business.

In addition, organizations will have to define whether to follow a "lift and shift" approach or a "refactor" approach. The good news is that here we will help you choose which option is best for the scenario.

Migrating from a large Hadoop environment to Databricks is a complex and large project. In this blog we will dive into different areas of the migration process and the challenges that the customer should plan in these areas: Administration, Data Migration, Data Processing, Security and Governance, and Data Consumption (tools and processes)

Over the past 10 years of big data analytics and data lakes, Hadoop has proven unscalable, overly complex (both onPremise and cloud versions) and unable to deliver to the business for ease of consumption or meet their innovation aspirations.

Migrating from Hadoop to Databricks will help you scale effectively, simplify your data platform and accelerate innovation with support for analytics, machine learning and AI.